1. Field of the Invention
The present invention relates to the petroleum industry, and more particularly to the development of underground reservoirs such as petroleum reservoirs or gas storage sites. In particular, the invention allows efficient planning of the development of a reservoir by selecting positions where new wells are to be drilled for which production potential will be a maximum.
2. Description of the Prior Art
Optimization and development of petroleum reservoirs is based on the most accurate possible description of the structure, the petrophysical properties, the fluid properties, etc., of the reservoir under study. A tool accounting for these aspects in an approximate way is a reservoir model. A reservoir model is a model of the subsoil which is representative of both its structure and its behavior. Generally, this type of model is represented in a computer and is then referred to as a “numerical model”. A reservoir model comprises a mesh or grid, generally three-dimensional, associated with one or more petrophysical property maps (porosity, permeability, saturation, etc.). The association assigns values of these petrophysical properties to each cell of the grid.
In order to be considered reliable, the reservoir model must agree with as much data collected in the field. The data are well-log data measured along the wells, measurements performed on rock samples taken in the wells, data determined from seismic acquisition surveys, production data such as oil and water flow rates, pressure data, etc. These data are not sufficient to precisely characterize the petrophysical property values to be assigned to the cells of the model. This is why a stochastic formalism is generally used. The petrophysical properties are considered as realizations of random functions. A possible image of the reservoir, that is, a model is then generated from geostatistical simulation techniques. Solving flow equations for this model provides production responses. These responses are then compared with the production data measured in the wells. The difference between the simulated responses and the data acquired in the field has to be minimized to increase predictivity of the reservoir model. This stage involves a calibration or optimization procedure, which in general requires substantial computation time because of its iterative process requiring a flow simulation per iteration. A single flow simulation often requires several hours of computation time.
When a model meeting the data measured in the field is finally obtained, it is used to predict the fluid displacements in the reservoir and to plan the future development of the field. For example, for mature fields, it must be possible to select the zones where new wells are to be drilled, either in order to produce oil by depletion drive or to inject a fluid that maintains the pressure at a sufficient level in the reservoir. The performance of a well at a given point can be assessed using the reservoir model by positioning the well in the desired position and carrying out a flow simulation. The performance of a well can be assessed from the amount of hydrocarbons it produces. Given that the final goal is maximization of the production or of the profitability of the field, it should be possible to test all possible well positions and to select the best one. Such an approach is inappropriate in practice because it involves a high computation time. One alternative is launching an optimization procedure intended to provide the best location possible for a well to optimize the production. However, this approach remains difficult to implement because it requires several thousand iterations.
The concept of production indicator maps, also referred to as quality maps in the literature, has been introduced in order to address in a practical manner the problem of positioning new wells in a reservoir. It is a two-dimensional map comprising a set of cells where each cell is associated with a real value that shows how a new well placed in the cell in question impacts the production or the net present value (NPV) in relation to the base case. The base case corresponds to the initial development scheme which is a scheme for which no new well is added (Da Cruz, P. S., Home, R. N., Deutsch, C., The Quality Map: A Tool for Reservoir Quantification and Decision Making, SPE ATCE, SPE 56578, Houston, Tex., USA, 1999). A production indicator defines an impact on the production of fluid (hydrocarbon) linked with the addition of a well in the cell considered.
To construct this map, a flow simulation can be performed for each cell where a well can be positioned. If the reservoir comprises NX and NY cells along axes X and Y, the total number of cells to be examined is NX×NY minus the number of non-active cells and of cells that already have a well for the base case. This approach requires a significant computation time insofar as NX×NY is large. Besides, the possible cells being considered one after the other, the interferences between the new wells are not taken into account.
In order to reduce the computation times, an interpolation approach has been considered (Cottini-Loureiro, A., Araujo, M., Optimized Well Location by Combination of Multiple Realization Approach and Quality Map Methods, SPE 95413, SPE ATCE, Dallas, Tex., USA, 9-12 Oct., 2005). A simulation is then carried out for some cells within the map and values in the other cells are estimated by interpolation. However, this approach does not account for the interferences between new wells.
The production indicator map quantifies, for each cell, the impact on a production indicator due to the addition of a well in this cell. It accounts for a single well. In order to add several wells and to account for the interferences between these wells, a sequential approach has been suggested. The wells are added one after the other. Each time a well is added, the quality map is updated in the region containing the position selected. A flow simulation is performed for each cell of the region under consideration. (Cheng, Y., McVay, D. A., Lee, W. J., A Practical Approach for Optimization of In fill Well Placement in Tight Gas Reservoirs, Journal of Natural Gas Science and Engineering, 1, 165-176, 2005). This solution requires many simulations and therefore a significant computation time.
Thus, none of the current methods provides a solution yielding precise results within a reduced computation time and accounting for the interferences with the added wells.